Aircraft Accidents
This essay by Mark Galasiewski originally appeared in The
Elliott Wave Theorist in January 2007
As observers of investor sentiment, we have noted that the
period from 1987 to early 1995 generated a severe pessimism. It resulted
from a Primary degree correction and then by a sequence of first and second
waves, where pessimism often maintains despite a net rise in stock prices.
Sentiment at the end of second waves often exceeds that seen at the beginning
of first waves (see Elliott
Wave Principles, pp. 79-80).
The other three graphs in Figure 1 show the high degrees of pessimism that
recurred during the period. Mutual fund managers on average held as much
as 12.9% of their portfolios in cash by 1990. Short positions in S&P
500 futures contracts held by small traders ballooned through 1994. And
bearish advisors polled by Investors Intelligence far exceeded bullish advisors
through most of 1988, 1990, and 1994-95.
The following study evidences negative mood manifestations during the 1987-95
period, fitting investor sentiment indicators even though the stock market
was drifting higher..
Figure 1
Aircraft Accidents
The investor Carl Icahn once said, “The fastest way to become a millionaire
is to invest in the airline industry as a billionaire.” Henry Hardevelt,
an analyst at Forrester Research, added, “The U.S. airline industry makes
NHL hockey matches look like fifth-grade recess. It’s brutal and bloody.
The sad truth is an investor could get a better return starting a Subway
sandwich shop than an airline.” And they were just talking about the financial
side of the business. We’re going to look at the physical side.
The social and economic contractions that take place during bear markets
are particularly hard on the people who fly, maintain, and guide airplanes.
They are forced to accomplish more with less time and resources in an already
highly competitive industry. We postulated that a negative social mood—held
by passengers, crew, maintenance workers and pilots alike—would tend to
increase the chances for aircraft accidents and that a positive social mood
would decrease them. Indeed that is the case.
Figure 2
Figure 2 shows an inverted graph of the annual number of U.S.
general aviation accidents per 100,000 flight hours along with the Dow Jones
Industrial Average for the past 30 years. It shows that as the Dow has risen,
aircraft safety has generally increased, while setbacks have occurred late
in periods of declining social mood. The correlation (R) of the
accident data to the log of annual closes in the DJIA is -91%, with a p-value
of 10-12, which means that the probability of obtaining such a high correlation
is extremely small, making this result highly statistically significant.
When using detrended data, we observe no correlation (R = 0.47%,
p = 0.98).
There are no comprehensive data available prior to 1975, so this series
may be too short to allow us to draw a conclusion just yet. But the surges
in the number of accidents leading up to 1982 and 1994 are conspicuous,
since they confirm the extremes registered by stock market sentiment indicators
in those years. If the negative social mood at those times is responsible
for those “air pockets,” then future bear markets should produce similar
spikes in the number of aircraft accidents. We will return to this study
at a future date.